A method, medium and device for analyzing influencing factors of resident green travel behavior

Abstract

This invention discloses a method, medium, and device for analyzing the influencing factors of residents' green travel behavior. Based on the theory of planned behavior and interpersonal relations theory, the method constructs a comprehensive theoretical model of the influencing factors of urban residents' green travel behavior from a static perspective. Secondly, based on a survey of 817 residents in the Yangtze River Delta region, structural equation modeling is used to empirically test the driving mechanisms of different categories of green travel behaviors among urban residents. Furthermore, a social interaction perspective is introduced, and a simulation model is constructed based on scale-free networks and opinion propagation dynamics to simulate and analyze the group response characteristics and dynamic trends of residents participating in various green travel behavior decisions under different scenario parameters. Finally, targeted policy recommendations are proposed to more effectively promote urban residents' practice of green travel.

Description

Technical Field

[0001] This invention relates to the field of green travel analysis technology, specifically to a method, medium, and equipment for analyzing factors influencing residents' green travel behavior. Background Technology

[0002] With the increasing demand for low-carbon urban transportation development, the simulation and prediction of residents' green travel behavior has gradually become an important technical direction in the fields of traffic management and policy-making. Existing technologies for analyzing residents' travel behavior mainly include big data statistical analysis methods, rule-based travel choice models, and some multi-agent behavioral simulation models. These technologies typically treat individuals as network nodes, describing the propagation process of information or opinions through the connections between nodes. They often focus on the impact of individual economic attributes, travel costs, and time costs on travel mode choices, resulting in a relatively simplified portrayal of the interaction relationships between individuals in social networks. While some technologies introduce social influence factors, they mostly assume a static network structure, failing to effectively reflect the non-equilibrium influence of a few highly influential individuals on group behavior in real society. Therefore, this invention aims to propose a simulation method and system for residents' green travel behavior based on scale-free networks. By introducing a network structure that conforms to the characteristics of social reality, and based on real-world data combined with the dynamic mechanism of opinion propagation under the principle of limited trust, this invention effectively simulates the dynamic evolution of residents' green travel intentions in social networks, thereby improving the accuracy and practicality of the simulation results. Summary of the Invention

[0003] The present invention proposes a method for analyzing the influencing factors of residents' green travel behavior, which can at least solve one of the technical problems in the background art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for analyzing the influencing factors of residents' green travel behavior includes the following steps: S100. Construct a static behavioral intention theory model based on the theory of planned behavior and interpersonal relations theory to construct behavioral influencing factors; S200: Design a data survey questionnaire to collect basic data, and combine it with structural equation modeling to construct a mechanism driving residents' green travel behavior. S300, based on the driving mechanism of residents' green travel behavior, introduces the social network evolution mechanism, and combines scale-free network and opinion propagation dynamics structure simulation model to analyze the decision-making response characteristics and dynamic change trends of residents' green travel behavior under different scenario parameters; S400: Based on the simulation model analysis results, complete the analysis of factors influencing residents' green travel behavior.

[0005] Furthermore, the method for constructing a static behavioral intention theory model of behavioral influencing factors in step S100 of the present invention includes: Set individual The willingness to engage in green travel will ultimately influence whether or not one chooses green travel, in terms of probability. To reflect the individual Individuals have a relatively strong intention to travel in a green way, and this is used to ultimately determine their green travel behavior. In Individual at any moment The travel behavior choice strategy space follows a Bernoulli distribution, that is, the green travel behavior choice strategy space is... ={0,1}; in, (t)=0 indicates that the individual Not choosing green travel behaviors (t)=1 indicates that an individual Choose green travel options; In addition, individuals at time t+1 Whether to choose green travel behavior depends on the individual at time t. The willingness to travel green is determined by the travel willingness model:

[0006] In individual at any moment The probability of choosing green travel corresponds to Conversely ,but The expected value is:

[0007] Based on the extended planning behavior theory model, in the context of individual moment The willingness to engage in green travel can be expressed as:

[0008] In the formula, AT indicates that the individual frequently and proactively seeks information about the environment; SNP indicates that most people around the individual choose green travel; SNM emphasizes the importance of media promotion of green travel and puts pressure on decision-makers; PBC represents the individual's active improvement of environmental issues due to intrinsic needs, which belongs to the category of endogenous psychology.

[0009] Furthermore, the basic data in step S200 of the present invention includes: The information on residents' household circumstances, green travel methods, access to green travel routes, and willingness to travel green is used to construct a mechanism that drives residents' green travel behavior.

[0010] Furthermore, the present invention constructs a mechanism to drive residents' green travel behavior, including: The willingness to adjust one's habits to travel green can be expressed as:

[0011] The willingness to engage in energy-investment-based green travel can be expressed as:

[0012] Interpersonal facilitation-based green travel behavior intentions can be expressed as:

[0013] Furthermore, the social evolution mechanism in step S300 of this invention includes: Condition 1: When the willingness to travel green When the green travel intention rate is not less than 3.3, which is the maximum value of 66%, individuals tend to engage in green travel behavior. Condition 2: In each round of information exchange, new nodes are constantly added, the total number of nodes is constantly increasing, and nodes are preferentially connected to nodes with higher connectivity. Condition 3: All individuals in a social network are heterogeneous. Therefore, heterogeneous individuals have different initial information regarding their choices of green travel behavior, represented as follows: The probability of an individual adopting advice from relatives and colleagues is used as... This represents the probability of adoption for each individual. The embedded network structures are also different; Condition 4: As social networks continue to change, the actor is not affected by all other actors connected to the actor.

[0014] Furthermore, the method for analyzing the decision-making response characteristics and dynamic changing trends of residents' green travel behavior under different scenario parameters in step S300 of the present invention includes: Assume that an individual's green travel information contains interpersonal facilitation-based green travel information values ​​and follows a normal distribution. ; in Individual at any moment Having green travel information ,make ∈[0,1]; individual exist The total number of adjacent entities at time t is denoted as Then the number of relatives, friends, and colleagues of individual i who choose green travel is The media influence coefficient is (0≤) ≤1); In the dynamic changes of social networks, when the first Rounds, two connected entities and The difference in green travel information is less than the threshold. At that time, they will adjust their information values ​​to each other, thereby influencing their willingness to engage in green travel behaviors, that is:

[0015] st

[0016] The actor is not affected by all other actors connected to it, that is, if the individual and The green travel information gap value is greater than the threshold. At that time, the actors whose information values ​​differ do not interact with each other, that is:

[0017] st

[0018] Individual exist At any given time, the habit-adjusted green travel behavior willingness model is as follows:

[0019] Individual exist At any given time, the willingness to engage in energy-investment-driven green travel behavior is as follows:

[0020] Individual exist At any given time, the willingness to engage in interpersonal-facilitated green travel behavior is as follows: .

[0021] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0022] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0023] As can be seen from the above technical solution, the present invention provides a method and system for analyzing the influencing factors of residents' green travel behavior. This method defines green travel behavior into three main categories: habit adjustment, energy-saving investment, and interpersonal promotion. Habit adjustment behavior refers to residents actively adjusting their travel modes in daily life, such as choosing public transportation, walking, cycling, or carpooling. Energy-saving investment behavior involves investing in high-efficiency transportation tools to reduce energy consumption, such as purchasing or driving new energy vehicles. Interpersonal promotion behavior includes actively persuading others to implement green travel and participating in green travel-related public welfare activities. By further classifying green travel behavior, we can not only gain a more comprehensive understanding of the influencing mechanisms of various behaviors, but also provide decision support for formulating more precise and effective policies and intervention measures. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method for analyzing the influencing factors of residents' green travel behavior in this invention; Figure 2 A schematic diagram of the social network state in the initial scenario at t=300; Figure 3 This is a schematic diagram illustrating the evolution of HMB, EIB, and IPB in the initial scenario; Figure 4 The diagram shows the evolution of HMB, EIB, and IPB when μ=0.8; Figure 5 for A schematic diagram illustrating the evolution of EIB and IPB when =0.5. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0026] like Figure 1 As shown in this embodiment, a method for analyzing factors influencing residents' green travel behavior includes the following steps: S100. Construct a static behavioral intention theory model based on the theory of planned behavior and interpersonal relations theory to construct behavioral influencing factors; S200: Design a data survey questionnaire to collect basic data, and combine it with structural equation modeling to construct a mechanism driving residents' green travel behavior. S300, based on the driving mechanism of residents' green travel behavior, introduces the social network evolution mechanism, and combines scale-free network and opinion propagation dynamics structure simulation model to analyze the decision-making response characteristics and dynamic change trends of residents' green travel behavior under different scenario parameters; S400: Based on the simulation model analysis results, complete the analysis of factors influencing residents' green travel behavior.

[0027] This study integrates the Theory of Planned Behavior (TPB) and the Theory of Interpersonal Relationships (TIB), classifying green travel behaviors into three categories: habit adjustment, energy-saving investment, and interpersonal facilitation. Based on questionnaire survey data from the Yangtze River Delta region, a comprehensive theoretical model of the influencing factors of urban residents' green travel behaviors is first constructed and empirically tested using structural equation modeling. Subsequently, scale-free networks and opinion propagation dynamics are introduced to construct a simulation model to simulate the group response characteristics and dynamic evolution trends of residents participating in various green travel behaviors under different scenario parameters.

[0028] The following provides a detailed explanation of each step: S100. Construct a static behavioral intention theory model based on the theory of planned behavior and interpersonal relations theory to construct behavioral influencing factors; The Theory of Planned Behavior (TPB) identifies attitudes, subjective norms, and perceived behavioral control as contributing factors to residents' intention to exhibit green behavior. Interpersonal Relationship Theory (IBT) integrates multiple factors such as attitudes, social norms, habits, situational factors, and motivations, providing a more complex and comprehensive framework for predicting behavior. Combined with attitudes in the Theory of Planned Behavior, it provides a more detailed analysis of individuals' perceptions of behavioral outcomes.

[0029] Behavioral intentions are closer to individual behavior than factors such as beliefs, attitudes, and emotions. That is, the individual... The willingness to engage in green travel will ultimately influence whether or not one chooses green travel, in terms of probability. To reflect the individual Individuals have a relatively strong intention to travel in a green way, and this is used to ultimately determine their green travel behavior.

[0030] In Individual at any moment The travel behavior choice strategy space follows a Bernoulli distribution, that is, the green travel behavior choice strategy space is... ={0,1}; in, (t)=0 indicates that the individual Not choosing green travel behaviors (t)=1 indicates that an individual Choose green travel options.

[0031] In addition, individuals at time t+1 Whether to choose green travel behavior depends on the individual at time t. The willingness to travel green is determined by the travel willingness model:

[0032] In individual at any moment The probability of choosing green travel corresponds to Conversely ,but The expected value is:

[0033] Based on the extended planning behavior theory model, in the context of individual moment The willingness to engage in green travel can be expressed as:

[0034] In the formula, AT refers to individuals actively seeking environmental information, thus the impact of adjustments to information from connected entities needs to be considered; SNP indicates that most people around an individual choose green travel, and changes in the behavior of those around them will affect the individual's travel intentions; SNM emphasizes the importance of media promotion of green travel, putting pressure on decision-makers, and the media influence coefficient needs to be considered. PBC represents an individual's active efforts to improve environmental problems due to intrinsic needs, which falls under the category of endogenous psychology.

[0035] S200: Design a data survey questionnaire to collect basic data, and combine it with structural equation modeling to construct a mechanism driving residents' green travel behavior. The data survey questionnaire includes data such as residents' household situation, residents' green travel methods, residents' methods of obtaining green travel routes, and residents' willingness to travel green. Data collected from the research questionnaire indicates that SNM (Social Sensitive Management) has a relatively insignificant impact on HMB (Hypermobility Mobility) (P<0.1). In other words, even if relevant management departments invest significant time, effort, and money in regulating media subjective factors, it will not significantly increase the intention to engage in habit-adjusted green travel. Therefore, this study does not consider the impact of SNM on habit-adjusted green travel behavior.

[0036] The willingness to adjust one's habits to travel green can be expressed as:

[0037] The willingness to engage in energy-investment-based green travel can be expressed as:

[0038] Interpersonal facilitation-based green travel behavior intentions can be expressed as:

[0039] S300, based on the driving mechanism of residents' green travel behavior, introduces the social network evolution mechanism, and combines scale-free network and opinion propagation dynamics structure simulation model to analyze the decision-making response characteristics and dynamic change trends of residents' green travel behavior under different scenario parameters; Individuals live in society, and their interactions and connections with each other influence their behavior. When choosing interpersonally-facilitated green travel, an individual's travel intentions are influenced by others in their social network. Scale-free networks are a representative form of complex networks, characterized by a few nodes with high connectivity and most nodes with fewer connections. This reflects that individuals with high influence in social reality are like large nodes in a network, capable of exerting a powerful influence on more nodes. Therefore, this paper primarily uses scale-free networks for social network analysis.

[0040] Based on scale-free networks, this study employs opinion propagation dynamics to describe the information interaction mechanism in social networks. In this study, individuals' initial attitudes refer to their initial willingness to engage in green travel, which was established based on questionnaire survey results. To characterize the dynamic evolution of the social network within the group over time, the following constraints are proposed based on the characteristics of social networks and Deffuant's principle of limited trust: Condition 1: When the willingness to travel green When the green travel intention rate is not less than 3.3, which is the maximum value of 66%, individuals tend to engage in green travel behavior.

[0041] Condition 2: Assume that new nodes are constantly added in each round of information exchange, the total number of nodes is constantly increasing, and nodes with higher connectivity are prioritized for connection.

[0042] Condition 3: All individuals in the social network are heterogeneous. Therefore, heterogeneous individuals have different initial information regarding their choices of green travel behavior, denoted as... The probability of an individual adopting advice from relatives and colleagues is used as... This represents the probability of adoption for each individual. The embedded network structures are also different.

[0043] Condition 4: With the continuous changes in social networks, the actor will not be affected by all the other actors connected to him. That is, if the actor's opinion is significantly different from that of his colleagues or relatives, there will be no interactive influence. This situation is more in line with reality.

[0044] Therefore, it is assumed that only when the difference in green travel information between two connected individuals does not exceed a threshold... Only then will the information and willingness of both parties regarding green travel be affected.

[0045] Based on the above constraints, each step represents a round of interaction. In the initial network C, individuals have some understanding of green travel methods, and their information mean is greater than 0. Complete green travel information promotes individuals' willingness to travel and has a positive impact on the information of other individuals during the evolution of the social network.

[0046] Assuming that an individual's green travel information contains interpersonal facilitation-based green travel information values ​​and follows a normal distribution, i.e. ; in Individual at any moment Having green travel information And let The larger the value of ∈[0,1], the more accurate the individual's understanding of travel modes and the stronger their willingness to travel green.

[0047] individual exist The total number of adjacent entities at time t is denoted as Then the number of his relatives, friends and colleagues who choose green travel is The media influence coefficient is (0≤) ≤1).

[0048] In the dynamic changes of social networks, when the first Rounds, two connected entities and The difference in green travel information is less than the threshold. At that time, they will adjust their information values ​​to each other, thereby influencing their willingness to engage in green travel behaviors, that is:

[0049] st

[0050] An actor is not affected by all other actors connected to it. That is, if an individual and The green travel information gap value is greater than the threshold. At that time, actors whose information values ​​differ significantly will not have an interactive impact on each other, that is:

[0051] st

[0052] Individual exist At any given time, their habit-adjusted green travel behavior intention model is as follows:

[0053] Individual exist At any given time, their willingness to engage in energy-investment-oriented green travel behavior is as follows:

[0054] Individual exist At any given time, their willingness to engage in interpersonal-facilitated green travel behavior is as follows:

[0055] S400: Based on the simulation model analysis results, complete the analysis of factors influencing residents' green travel behavior; Based on the analysis of the survey data results using specific examples, some initial values ​​for the dynamic propagation of opinions in scale-free networks are set as follows: the degree of the scale-free network is 3, and the initial number of participants is 20.

[0056] The following parameters were obtained based on the mean of the questionnaire survey data: =4.18, =3.64, =3.80, =3.90. Furthermore, residents' initial willingness to adjust their green travel behavior follows a normal distribution. ~N (3.99, 0.54), ~N(3.84, 0.89), ~N (3.77, 0.68); the initial information also follows a normal distribution. ~N (0.9, 1.3). Clearly, the information interaction threshold is greater than 0. Let... =0.3. The probability of being influenced by others is... It follows a normal distribution. ~N(0,1). Media Influence Coefficient .

[0057] This evolution process can be simulated using a multi-agent approach with Netlogo: Initial habitual adjustment-type green travel behavior social network status such as Figure 2 As shown in (a), each dot represents a participant; green dots indicate individuals who have chosen habit-adjusted green travel behaviors, while red dots indicate individuals who have chosen non-green behaviors. As the social network grows, new individuals gradually join, represented by yellow dots. Figure 2 As shown in (b), when the social network has developed to 300 rounds, 213 nodes have a degree of 1, 54 nodes have a degree of 2, and only about 16 nodes have a degree of 7-26. This indicates that most social connections are concentrated on a few nodes, exhibiting significant scale-free network characteristics, and verifying the consistency between the model and the evolution of urban residents' travel behavior.

[0058] Evolutionary analysis of population selection in the initial scenario: like Figure 3 As shown, under the same scale-free network framework (initial n=20, average degree k=3, information threshold μ=0.30, media influence coefficient ε=0), the information of the three types of green travel behaviors converged to 0.55–0.59 within 300 rounds. However, the subsequent evolution produced a step-like result due to the difference in the initial willingness distribution and cost threshold: ① The habit adjustment type, with the highest initial willingness of 3.99, stabilized the information mean at 0.59 in only about 100 rounds. Figure 3 (b) and stabilize group cognition at the "ideal" level during the same period; at this time, the average willingness remained at 3.38 ( Figure 3 (a)), the adoption rate climbed from 51% to 59% ( Figure 3 (c)), then the fluctuations were minimal, indicating that the rapid dissemination of information increased residents' recognition of the benefits of "fine-tuning daily habits," meaning that low-cost habit fine-tuning is the most effective way to transform "knowing" into "doing." ② Energy investment type Although information also stabilized after 300 rounds (0.58) Figure 3 (e)), but high capital and facility costs caused the willingness to decline from 3.54 to 2.52. Figure 3 (d) The final adoption rate remained at 8.4% ( Figure 3 (f) A gap appears between "sufficient cognition and hindered action." ③ The willingness to facilitate interpersonal relationships decreased from 3.69 to 2.18. Figure 3 (g) The adoption rate gradually increased with the spread of information but stopped at 11.7%. Figure 3 (i) is similar to energy investment. Overall, it indicates that social information interaction can rapidly narrow the cognitive gap between the three types of behavior, but it struggles to cross the willingness threshold, ultimately leading to "habit adjustment." The steady-state adoption pattern of "energy investment ≈ interpersonal facilitation" further illustrates that behavior is determined by individual will.

[0059] The impact of information interaction threshold: like Figure 4 As shown, after raising the information interaction threshold from μ=0.3 to μ=0.8, social networks allow individuals with greater differences to exchange viewpoints. All three types of green travel behaviors exhibit a chain reaction of "faster cognitive convergence—overall increase in willingness—synchronous rise in adoption rate": ① For habit adjustment type, after 300 rounds, the average information value stabilized at 0.57, the group willingness increased from 3.03 to 3.59, and the adoption rate subsequently increased from 59.1% to 63.6%. Figure 4 (a–c) This further confirms that low-cost behavior is the most effective way to translate cognitive advantages into action; ② Energy investment-related information converged to 0.56 within 300 rounds, but high costs still suppressed willingness (2.62), although the adoption rate increased from 15.4% to 21.5% (see Figure 4(d–f) indicates that more intensive information contact partially bridged the "cognition-action" gap; ③ After 300 iterations, the interpersonal facilitation intention stabilized at 2.58, but due to the social reinforcement effect, the adoption rate surged from 8.7% to 25.9% ( Figure 4 (g–i), similar to energy investment-type. Overall, expanding the information interaction threshold increases the penetration of green travel among the population, and is more helpful for behaviors with high costs or weak external incentives.

[0060] The impact of media publicity: like Figure 5 As shown, when the media influence coefficient ε is increased to 0.5, authoritative propaganda immediately becomes the primary driver of high-cost or under-incentivized behavior: on the one hand, the average willingness for energy investment increases from 2.38 to approximately 2.73, and the adoption rate jumps to 31.1% (see...). Figure 5 (a–c) demonstrates that a positive media orientation can effectively bridge the "cognitive-economic threshold" gap; on the other hand, the interpersonal facilitation type surged from only 26.9% to 52.4% (see a–c). Figure 5 The increase (d–f) far exceeds the effect of simply relaxing the interaction threshold, indicating that media intervention, by amplifying the pressure of social norms, significantly enhances residents' motivation to promote green travel to others. Overall, compared to raising the interaction threshold, media intervention with ε=0.5 has the greatest marginal contribution to adoption rate, especially showing a multiplier effect on energy investment-driven and interpersonal promotion-driven behaviors, which are constrained by costs or external incentives. Therefore, media guidance and public awareness campaigns are indispensable for rapidly expanding the use of new energy vehicles or promoting green travel within social circles in the early stages.

[0061] In summary, the method of this invention combines static causal testing of structural equation modeling with dynamic simulation of scale-free networks, revealing the multi-layered driving logic of urban residents' green travel behavior and clarifying the psychological differences between different behavioral types. The main conclusions are as follows: First, attitudes (AT), subjective norms (peer SNP / media SNM), and perceived behavioral control (PBC) can all positively influence green travel behavior through behavioral intentions, but their modes of action are significantly heterogeneous: habit adjustment behavior relies more on PBC and media norms, interpersonal facilitation behavior is mainly driven by attitudes and peer norms, and energy investment behavior is affected by both attitudes and subjective norms, and its intention-to-behavior conversion rate is relatively low, suggesting that this type may be more affected by external constraints.

[0062] Secondly, the formation of green travel exhibits a multi-chain mediation mechanism: "antecedent cognition → (attitude / subjective norms) → willingness → behavior". Upstream variables such as perceived behavioral outcomes, group identity, and policy perception first play a mediating role through attitudes or subjective norms, and then willingness further promotes behavioral transformation. This indicates that a single psychological variable is difficult to directly induce action, and willingness plays a key bridging role in the process.

[0063] Furthermore, network simulation highlights the importance of social structures and media externalities. Lowering the information interaction threshold can significantly amplify information diffusion and improve group adoption levels; increasing the media influence coefficient can significantly improve the gap between intention and behavior, especially for behavior types with low initial intention levels.

[0064] Therefore, policymakers should design differentiated and comprehensive intervention programs based on the psychological driving characteristics of different behavioral types to promote the continuous improvement of urban green travel. Specifically: (1) Implement differentiated social mobilization and empowerment strategies. Empirical results reveal that habit adjustment behaviors are most sensitive to perceived behavioral control and media norms, interpersonal facilitation behaviors are mainly driven by peer norms and attitudes, while energy investment behaviors rely on both attitudes and subjective norms. Accordingly, the government and communities can launch a tiered dissemination plan: ① For habit adjustment behaviors, focus on providing real-time bus and cycling navigation, green points rankings, and other instant feedback tools to improve residents' perception of "what they can do"; ② For interpersonal facilitation behaviors, strengthen group identity and peer demonstration through storytelling, team competitions, and community "green travel promotion ambassadors"; ③ For energy investment behaviors, solidify positive attitudes and shape social norms by releasing new energy vehicle usage scenarios and social responsibility cases through authoritative institutions. With the above targeted content, and with opinion leaders, community backbones, and other key nodes responsible for dissemination, the positive impact can be quickly amplified within the same network.

[0065] (2) Lower the information interaction threshold and strengthen cross-group diffusion. Simulation analysis shows that raising the information interaction threshold significantly improves the steady-state adoption rate of the three types of behaviors. It is recommended that the government build open online and offline interactive platforms (such as green travel-themed forums, debates, check-in challenges, etc.) to encourage supporters and apathetic individuals to communicate face-to-face or online; at the same time, develop low-threshold sharing tools, such as "one-click generation of low-carbon travel trajectory posters" or "green travel diary" mini-programs, so that residents can easily showcase their personal practices. By increasing the frequency and depth of contact with heterogeneous groups, the aggregation of viewpoints and the diffusion of positive demonstrations can be accelerated, expanding the social base of green travel.

[0066] (3) Leverage the multiplier effect of multi-level media. Media subjective norms have shown significant driving force in both empirical and simulation studies, especially for energy investment-oriented and interpersonal promotion-oriented behaviors. The government should collaborate with mainstream media and new media platforms to launch multi-channel, segmented special reports, short videos, and real-time data visualizations on green travel; at the same time, it should initiate interactive topics such as "Green Travel Check-in" and "Low-Carbon Challenge" on social networks to create visualized symbols of collective action. High-frequency, positive, and diverse media touchpoints can enhance the intensity of public discussion and social norms, shorten the "will-behavior" distance, and achieve a large-scale upgrade in behavior diffusion.

[0067] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0068] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0069] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the methods for analyzing the influencing factors of residents' green travel behavior in the above embodiments.

[0070] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0071] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0072] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0073] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the influencing factors of residents' green travel behavior, characterized in that, Includes the following steps: S100. Construct a static behavioral intention theory model based on the theory of planned behavior and interpersonal relations theory to construct behavioral influencing factors; S200: Design a data survey questionnaire to collect basic data, and combine it with structural equation modeling to construct a mechanism driving residents' green travel behavior. S300, based on the driving mechanism of residents' green travel behavior, introduces the social network evolution mechanism, and combines scale-free network and opinion propagation dynamics structure simulation model to analyze the decision-making response characteristics and dynamic change trends of residents' green travel behavior under different scenario parameters; S400: Based on the simulation model analysis results, complete the analysis of factors influencing residents' green travel behavior.

2. The method for analyzing factors influencing residents' green travel behavior according to claim 1, characterized in that... The method for constructing a static behavioral intention theory model of behavioral influencing factors in step S100 includes: Set individual The willingness to engage in green travel will ultimately influence whether or not one chooses green travel, in terms of probability. To reflect the individual Individuals have a relatively strong intention to travel in a green way, and this is used to ultimately determine their green travel behavior. In Individual at any moment The travel behavior choice strategy space follows a Bernoulli distribution, that is, the green travel behavior choice strategy space is... ={0,1}; in, (t)=0 indicates that the individual Not choosing green travel behaviors (t)=1 indicates that an individual Choose green travel options; In addition, individuals at time t+1 Whether to choose green travel behavior depends on the individual at time t. The willingness to travel green is determined by the travel willingness model: In individual at any moment The probability of choosing green travel corresponds to Conversely ,but The expected value is: Based on the extended planning behavior theory model, in the context of individual moment The willingness to engage in green travel can be expressed as: In the formula, AT indicates that the individual frequently and proactively seeks information about the environment; SNP indicates that most people around the individual choose green travel; SNM emphasizes the importance of media promotion of green travel and puts pressure on decision-makers; PBC represents the individual's active improvement of environmental issues due to intrinsic needs, which belongs to the category of endogenous psychology.

3. The method for analyzing factors influencing residents' green travel behavior according to claim 1, characterized in that, The basic data in step S200 includes: The information on residents' household circumstances, green travel methods, access to green travel routes, and willingness to travel green is used to construct a mechanism that drives residents' green travel behavior.

4. The method for analyzing factors influencing residents' green travel behavior according to claim 3, characterized in that, Building a mechanism to drive residents' green travel behavior includes: The willingness to adjust one's habits to travel green can be expressed as: The willingness to engage in energy-investment-based green travel can be expressed as: Interpersonal facilitation-based green travel behavior intentions can be expressed as: 。 5. The method for analyzing factors influencing residents' green travel behavior according to claim 1, characterized in that, The social evolution mechanisms in step S300 include: Condition 1: When the willingness to travel green When the green travel intention rate is not less than 3.3, which is the maximum value of 66%, individuals tend to engage in green travel behavior. Condition 2: In each round of information exchange, new nodes are constantly added, the total number of nodes is constantly increasing, and nodes are preferentially connected to nodes with higher connectivity. Condition 3: All individuals in a social network are heterogeneous. Therefore, heterogeneous individuals have different initial information regarding their choices of green travel behavior, represented as follows: The probability of an individual adopting advice from relatives and colleagues is used as... This represents the probability of adoption for each individual. The embedded network structures are also different; Condition 4: As social networks continue to change, the actor is not affected by all other actors connected to the actor.

6. The method for analyzing factors influencing residents' green travel behavior according to claim 1, characterized in that, Step S300 includes the following methods for analyzing the characteristics and dynamic trends of residents' green travel behavior decision-making responses under different scenario parameters: Assume that an individual's green travel information contains interpersonal facilitation-based green travel information values ​​and follows a normal distribution. ; in Individual at any moment Having green travel information ,make ∈[0,1]; individual exist The total number of adjacent entities at time t is denoted as Then the number of relatives, friends, and colleagues of individual i who choose green travel is The media influence coefficient is (0≤) ≤1); In the dynamic changes of social networks, when the first Rounds, two connected entities and The difference in green travel information is less than the threshold. At that time, they will adjust their information values ​​to each other, thereby influencing their willingness to engage in green travel behaviors, that is: s.t. The actor is not affected by all other actors connected to it, that is, if the individual and The green travel information gap value is greater than the threshold. At that time, the actors whose information values ​​differ do not interact with each other, that is: s.t. Individual exist At any given time, the habit-adjusted green travel behavior willingness model is as follows: Individual exist At any given time, the willingness to engage in energy-investment-driven green travel behavior is as follows: Individual exist At any given time, the willingness to engage in interpersonal-facilitated green travel behavior is as follows: 。 7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the method as described in any one of claims 1 to 6.

8. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 6.

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